Articolo in rivista, 2023, ENG, 10.1016/j.eswa.2023.120874
Luca Bacco, Felice Dell'Orletta, Huiyuan Lai, Mario Merone, Malvina Nissim
Università Campus Bio-Medico di Roma, Department of Engineering; Istituto di Linguistica Computazionale "Antonio Zampolli"; University of Groningen, The Netherlands; Università Campus Bio-Medico di Roma, Department of Engineering; University of Groningen, The Netherlands;
Physicians and patients often come from different backgrounds and have varying levels of education, which can result in communication difficulties in the healthcare process. To address this expertise gap, we present a "Text Style Transfer" system. Our system uses Semantic Textual Similarity techniques based on Sentence Transformers models to create pseudo-parallel datasets from a large, non-parallel corpus of lay and expert texts. This approach allowed us to train a denoising autoencoder model (BART), overcoming the limitations of previous systems. Our extensive analysis, which includes both automatic metrics and human evaluations from both lay (patients) and expert (physicians) individuals, shows that our system outperforms state-of-the-art models and is comparable to human-provided gold references in some cases.
Expert systems with applications 233 , pp. 1–18
Healthcare, Natural language processing, Text style transfer, Text simplification
ILC – Istituto di linguistica computazionale "Antonio Zampolli"
ID: 488201
Year: 2023
Type: Articolo in rivista
Creation: 2023-11-06 18:46:16.000
Last update: 2023-11-07 08:25:05.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.eswa.2023.120874
URL: https://www.sciencedirect.com/science/article/pii/S0957417423013763
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:488201
DOI: 10.1016/j.eswa.2023.120874